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Ensemble of local and global information for Protein-Ligand Binding Affinity Prediction
Comput Biol Chem. 2023 Oct 20;107:107972. doi: 10.1016/j.compbiolchem.2023.107972. Online ahead of print.ABSTRACTAccurately predicting protein-ligand binding affinities is crucial for determining molecular properties and understanding their physical effects. Neural networks and transformers are the predominant methods for sequence modeling, and both have been successfully applied independently for protein-ligand binding affinity prediction. As local and global information of molecules are vital for protein-ligand binding affinity prediction, we aim to combine bi-directional gated recurrent unit (BiGRU) and convolutional ne...
Source: Computational Biology and Chemistry - October 26, 2023 Category: Bioinformatics Authors: Gaili Li Yongna Yuan Ruisheng Zhang Source Type: research

Ensemble of local and global information for Protein-Ligand Binding Affinity Prediction
Comput Biol Chem. 2023 Oct 20;107:107972. doi: 10.1016/j.compbiolchem.2023.107972. Online ahead of print.ABSTRACTAccurately predicting protein-ligand binding affinities is crucial for determining molecular properties and understanding their physical effects. Neural networks and transformers are the predominant methods for sequence modeling, and both have been successfully applied independently for protein-ligand binding affinity prediction. As local and global information of molecules are vital for protein-ligand binding affinity prediction, we aim to combine bi-directional gated recurrent unit (BiGRU) and convolutional ne...
Source: Computational Biology and Chemistry - October 26, 2023 Category: Bioinformatics Authors: Gaili Li Yongna Yuan Ruisheng Zhang Source Type: research

Ensemble of local and global information for Protein-Ligand Binding Affinity Prediction
Comput Biol Chem. 2023 Oct 20;107:107972. doi: 10.1016/j.compbiolchem.2023.107972. Online ahead of print.ABSTRACTAccurately predicting protein-ligand binding affinities is crucial for determining molecular properties and understanding their physical effects. Neural networks and transformers are the predominant methods for sequence modeling, and both have been successfully applied independently for protein-ligand binding affinity prediction. As local and global information of molecules are vital for protein-ligand binding affinity prediction, we aim to combine bi-directional gated recurrent unit (BiGRU) and convolutional ne...
Source: Computational Biology and Chemistry - October 26, 2023 Category: Bioinformatics Authors: Gaili Li Yongna Yuan Ruisheng Zhang Source Type: research

Ensemble of local and global information for Protein-Ligand Binding Affinity Prediction
Comput Biol Chem. 2023 Oct 20;107:107972. doi: 10.1016/j.compbiolchem.2023.107972. Online ahead of print.ABSTRACTAccurately predicting protein-ligand binding affinities is crucial for determining molecular properties and understanding their physical effects. Neural networks and transformers are the predominant methods for sequence modeling, and both have been successfully applied independently for protein-ligand binding affinity prediction. As local and global information of molecules are vital for protein-ligand binding affinity prediction, we aim to combine bi-directional gated recurrent unit (BiGRU) and convolutional ne...
Source: Computational Biology and Chemistry - October 26, 2023 Category: Bioinformatics Authors: Gaili Li Yongna Yuan Ruisheng Zhang Source Type: research

Ensemble of local and global information for Protein-Ligand Binding Affinity Prediction
Comput Biol Chem. 2023 Oct 20;107:107972. doi: 10.1016/j.compbiolchem.2023.107972. Online ahead of print.ABSTRACTAccurately predicting protein-ligand binding affinities is crucial for determining molecular properties and understanding their physical effects. Neural networks and transformers are the predominant methods for sequence modeling, and both have been successfully applied independently for protein-ligand binding affinity prediction. As local and global information of molecules are vital for protein-ligand binding affinity prediction, we aim to combine bi-directional gated recurrent unit (BiGRU) and convolutional ne...
Source: Computational Biology and Chemistry - October 26, 2023 Category: Bioinformatics Authors: Gaili Li Yongna Yuan Ruisheng Zhang Source Type: research

Ensemble of local and global information for Protein-Ligand Binding Affinity Prediction
Comput Biol Chem. 2023 Oct 20;107:107972. doi: 10.1016/j.compbiolchem.2023.107972. Online ahead of print.ABSTRACTAccurately predicting protein-ligand binding affinities is crucial for determining molecular properties and understanding their physical effects. Neural networks and transformers are the predominant methods for sequence modeling, and both have been successfully applied independently for protein-ligand binding affinity prediction. As local and global information of molecules are vital for protein-ligand binding affinity prediction, we aim to combine bi-directional gated recurrent unit (BiGRU) and convolutional ne...
Source: Computational Biology and Chemistry - October 26, 2023 Category: Bioinformatics Authors: Gaili Li Yongna Yuan Ruisheng Zhang Source Type: research

Ensemble of local and global information for Protein-Ligand Binding Affinity Prediction
Comput Biol Chem. 2023 Oct 20;107:107972. doi: 10.1016/j.compbiolchem.2023.107972. Online ahead of print.ABSTRACTAccurately predicting protein-ligand binding affinities is crucial for determining molecular properties and understanding their physical effects. Neural networks and transformers are the predominant methods for sequence modeling, and both have been successfully applied independently for protein-ligand binding affinity prediction. As local and global information of molecules are vital for protein-ligand binding affinity prediction, we aim to combine bi-directional gated recurrent unit (BiGRU) and convolutional ne...
Source: Computational Biology and Chemistry - October 26, 2023 Category: Bioinformatics Authors: Gaili Li Yongna Yuan Ruisheng Zhang Source Type: research

Ensemble of local and global information for Protein-Ligand Binding Affinity Prediction
Comput Biol Chem. 2023 Oct 20;107:107972. doi: 10.1016/j.compbiolchem.2023.107972. Online ahead of print.ABSTRACTAccurately predicting protein-ligand binding affinities is crucial for determining molecular properties and understanding their physical effects. Neural networks and transformers are the predominant methods for sequence modeling, and both have been successfully applied independently for protein-ligand binding affinity prediction. As local and global information of molecules are vital for protein-ligand binding affinity prediction, we aim to combine bi-directional gated recurrent unit (BiGRU) and convolutional ne...
Source: Computational Biology and Chemistry - October 26, 2023 Category: Bioinformatics Authors: Gaili Li Yongna Yuan Ruisheng Zhang Source Type: research

Protein-DNA interface hotspots prediction based on fusion features of embeddings of protein language model and handcrafted features
Comput Biol Chem. 2023 Oct 10;107:107970. doi: 10.1016/j.compbiolchem.2023.107970. Online ahead of print.ABSTRACTThe identification of hotspot residues at the protein-DNA binding interfaces plays a crucial role in various aspects such as drug discovery and disease treatment. Although experimental methods such as alanine scanning mutagenesis have been developed to determine the hotspot residues on protein-DNA interfaces, they are both inefficient and costly. Therefore, it is highly necessary to develop efficient and accurate computational methods for predicting hotspot residues. Several computational methods have been devel...
Source: Computational Biology and Chemistry - October 22, 2023 Category: Bioinformatics Authors: Xiang Li Gang-Ao Wang Zhuoyu Wei Hong Wang Xiaolei Zhu Source Type: research

ResBiGAAT: Residual Bi-GRU with attention for protein-ligand binding affinity prediction
This study presents ResBiGAAT, a novel deep learning model that combines a deep Residual Bidirectional Gated Recurrent Unit with two-sided self-attention mechanisms. ResBiGAAT leverages protein and ligand sequence-level features and their physicochemical properties to efficiently predict protein-ligand binding affinity. Through rigorous evaluation using 5-fold cross-validation, we demonstrate the performance of our proposed approach. The model exhibits competitive performance on an external dataset, highlighting its generalizability. Our publicly available web interface, located at resbigaat.streamlit.app, allows users to ...
Source: Computational Biology and Chemistry - October 22, 2023 Category: Bioinformatics Authors: Gelany Aly Abdelkader Soualihou Ngnamsie Njimbouom Tae-Jin Oh Jeong-Dong Kim Source Type: research

Protein-DNA interface hotspots prediction based on fusion features of embeddings of protein language model and handcrafted features
Comput Biol Chem. 2023 Oct 10;107:107970. doi: 10.1016/j.compbiolchem.2023.107970. Online ahead of print.ABSTRACTThe identification of hotspot residues at the protein-DNA binding interfaces plays a crucial role in various aspects such as drug discovery and disease treatment. Although experimental methods such as alanine scanning mutagenesis have been developed to determine the hotspot residues on protein-DNA interfaces, they are both inefficient and costly. Therefore, it is highly necessary to develop efficient and accurate computational methods for predicting hotspot residues. Several computational methods have been devel...
Source: Computational Biology and Chemistry - October 22, 2023 Category: Bioinformatics Authors: Xiang Li Gang-Ao Wang Zhuoyu Wei Hong Wang Xiaolei Zhu Source Type: research

ResBiGAAT: Residual Bi-GRU with attention for protein-ligand binding affinity prediction
This study presents ResBiGAAT, a novel deep learning model that combines a deep Residual Bidirectional Gated Recurrent Unit with two-sided self-attention mechanisms. ResBiGAAT leverages protein and ligand sequence-level features and their physicochemical properties to efficiently predict protein-ligand binding affinity. Through rigorous evaluation using 5-fold cross-validation, we demonstrate the performance of our proposed approach. The model exhibits competitive performance on an external dataset, highlighting its generalizability. Our publicly available web interface, located at resbigaat.streamlit.app, allows users to ...
Source: Computational Biology and Chemistry - October 22, 2023 Category: Bioinformatics Authors: Gelany Aly Abdelkader Soualihou Ngnamsie Njimbouom Tae-Jin Oh Jeong-Dong Kim Source Type: research

Protein-DNA interface hotspots prediction based on fusion features of embeddings of protein language model and handcrafted features
Comput Biol Chem. 2023 Oct 10;107:107970. doi: 10.1016/j.compbiolchem.2023.107970. Online ahead of print.ABSTRACTThe identification of hotspot residues at the protein-DNA binding interfaces plays a crucial role in various aspects such as drug discovery and disease treatment. Although experimental methods such as alanine scanning mutagenesis have been developed to determine the hotspot residues on protein-DNA interfaces, they are both inefficient and costly. Therefore, it is highly necessary to develop efficient and accurate computational methods for predicting hotspot residues. Several computational methods have been devel...
Source: Computational Biology and Chemistry - October 22, 2023 Category: Bioinformatics Authors: Xiang Li Gang-Ao Wang Zhuoyu Wei Hong Wang Xiaolei Zhu Source Type: research

ResBiGAAT: Residual Bi-GRU with attention for protein-ligand binding affinity prediction
This study presents ResBiGAAT, a novel deep learning model that combines a deep Residual Bidirectional Gated Recurrent Unit with two-sided self-attention mechanisms. ResBiGAAT leverages protein and ligand sequence-level features and their physicochemical properties to efficiently predict protein-ligand binding affinity. Through rigorous evaluation using 5-fold cross-validation, we demonstrate the performance of our proposed approach. The model exhibits competitive performance on an external dataset, highlighting its generalizability. Our publicly available web interface, located at resbigaat.streamlit.app, allows users to ...
Source: Computational Biology and Chemistry - October 22, 2023 Category: Bioinformatics Authors: Gelany Aly Abdelkader Soualihou Ngnamsie Njimbouom Tae-Jin Oh Jeong-Dong Kim Source Type: research

Protein-DNA interface hotspots prediction based on fusion features of embeddings of protein language model and handcrafted features
Comput Biol Chem. 2023 Oct 10;107:107970. doi: 10.1016/j.compbiolchem.2023.107970. Online ahead of print.ABSTRACTThe identification of hotspot residues at the protein-DNA binding interfaces plays a crucial role in various aspects such as drug discovery and disease treatment. Although experimental methods such as alanine scanning mutagenesis have been developed to determine the hotspot residues on protein-DNA interfaces, they are both inefficient and costly. Therefore, it is highly necessary to develop efficient and accurate computational methods for predicting hotspot residues. Several computational methods have been devel...
Source: Computational Biology and Chemistry - October 22, 2023 Category: Bioinformatics Authors: Xiang Li Gang-Ao Wang Zhuoyu Wei Hong Wang Xiaolei Zhu Source Type: research